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Lucky factors
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PRE
AI
DOI：10.1016/j.jfineco.2021.04.014.png)
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摘要
En 中文
Identifying the factors that drive the cross-section of expected returns is challenging for at least three reasons. First, the choice of testing approach (time series versus cross-sectional) will deliver different sets of factors. Second, varying test portfolio sorts changes the impor-tance of candidate factors. Finally, given the hundreds of factors that have been proposed, test multiplicity must be dealt with. We propose a new method that makes measured progress in addressing these key challenges. We apply our method in a panel regression setting and shed some light on the puzzling empirical result that the market factor drives the bulk of the variance of stock returns, but is often knocked out in cross-sectional tests. In our setup, the market factor is not eliminated. Further, we bypass arbitrary portfolio sorts and instead execute our tests on individual stocks with no loss in power. Finally, our bootstrap implementation, which allows us to impose the null hypothesis of no cross-sectional explanatory power, naturally controls for the multiple testing problem. (c) 2021 Elsevier B.V. All rights reserved.
Keyword:
Factors
Factor selection
Variable selection
Bootstrap
Data mining
Orthogonalization
Multiple testing
Predictive regressions
Fama-MacBeth
GRS
期刊
IF:
12
论文数:
3.8K
被引数:
5.5W
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